Pathology imaging is one of the last fields in medical imaging yet to be digitized. Compared to other well-developed medical imaging modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), digitized pathology images are characterized by super-high image resolution, non-uniform texture patterns, and densely-structured segments. In addition, the diversity of cancer types leads to constantly changing image patterns, which makes it even more challenging to develop fully-automatic image classification algorithms.
Digitized pathology images are created from tissue samples stained with different methods for different diagnosing purposes, such as H&E (hematoxylin and eosin) and IHC (immunohistochemical) staining. Both of these staining methods are widely used in pathology, and H&E staining is particularly common for use in biopsy of suspected cancerous tissue.
Conventional pathology image analysis methods utilize human labor to individually examine and label the stained pathology images. This practice requires a great deal of human labor, is time consuming, and is subject to the subjectivity of the pathologist.
To date, digitalization of pathology image analysis has seen only small amounts of development. Some conventional image recognition frameworks rely on single-step methods for model training and image classification. A model building phase of a conventional technique may involve building models based on training data sets that have been labeled with ground truth labels by a human analyst. In such conventional techniques, the pixels of a training data set may be labeled according to a ground truth having multiple dimensions. For instance, each pixel of a digitized pathology image may be labeled by tissue type, where there are multiple tissue types from which to select. The pixels of a digital training data set may also be characterized according to multiple features. Each of these multiple features may have multiple dimensions. These multiple features may then be concatenated to yield a high-dimensional data set that describes each pixel. An image recognition model is then generated with machine learning techniques using the high-dimensional data set and the multi-dimensional ground truth. Because each pixel may be described by hundreds of feature dimensions as well as multiple ground truth dimensions, in an image containing millions of pixels, the quantity of data rapidly becomes difficult to process. The requirement of a computer to keep all of the features and ground truth dimensions in memory at once leads to delays in processing and high memory requirements. Conventional training techniques may take a long time and, because of processor requirements, may use only small subsets of training data to train the models. Classification phases of conventional single-step image recognition frameworks suffer from similar problems.
It is therefore desirable to provide a faster and more efficient multi-step image recognition framework. Such a multi-step image recognition framework may gradually build models by working with a limited number of ground truth dimensions and a elected group of features in each step. Multi-step image recognition frameworks may also utilize a multi-layer feature extraction method in order to reduce the pixel feature dimension. By reducing computing power and memory requirements, larger portions of training data may be used to train the multi-step image recognition models proposed herein.